Identifying Follow-Correlation Itemset-Pairs
Shichao Zhang, Jilian Zhang, Xiaofeng Zhu, Zifang Huang · Proceedings · 2006
An association rule ArarrB is useful to predict that B will likely occur when A occurs. This is a classical association rule. In real world applications, such as bioinformatics and medical research, there are many follow correlations between itemsets A and B: B likely occurs n times after A occurred m times, wrote tom, BN>. We refer to this follow-correlation as P3.1 itemset-pairs because3, B1> like that in the example ( Example 2) should be uninterested in association analysis. This paper designs an efficient algorithm for identifying P3.1 itemset-pairs in sequential data. We experimentally evaluate our approach, and demonstrate that the proposed approach is efficient and promising.